Technical Field
The present invention relates to the management of physical systems, and, more particularly, to the management of components of physical systems using a time series aging profiling engine.
Description of the Related Art
Long term operation of physical systems causes degradation of their components, which may cause, for example, overall system performance degradation, component malfunctions, or even failure of the whole system. Degradations (e.g., aging) may lead to condition changes in mean values, amplitudes, and/or frequencies in the sensed signals of the system, and is an unavoidable property of any system. However, well-timed detection and profiling of aging trends may help to avoid consequences and problems degradation/aging may cause, and is a crucial task when using modern machinery because if one component of a system fails, it may lead to the stoppage of the whole system. Furthermore even short interruptions in production processes (e.g., because of component degradation/failure) may result in, for example, huge money losses and serious business related issues.
Problems with conventional trend analysis and extraction for time series have attracted significant attention recently, as aging trends are important for avoiding component failures in physical systems. However, conventional systems and methods are only able to focus on extracting general trend behavior from time series, and none of them can specifically addresses aging detection and profiling without having prior knowledge of time series properties (e.g., seasonality, level of noise, etc.).
Aging trend extraction may be employed to detect and analyze aging phenomena and/or degrading behavior in time series obtained from sensors monitoring physical systems (e.g., machinery). However, such problems are complicated and computation intensive because systems are generally operated according to some specific patterns. Moreover, the aging behavior is generally invisible (e.g., undetectable by humans) due to, for example, high noise and operational signals, and thus is generally too small to be detected using conventional systems and methods.